- Introduction
- What we hope to learn, what we expect to cover
- Why modeling?
- discovery (choose better experiments [sensitivity and uncertainty analyses]; do the impossible [ask "what if?"])
- design (predict and simulate)
- Python (using ipython in browser), Anaconda
- How to take notes (concepts? functions? demos?)
- Object types. (int/float/str/containers/boolean)
- Duck typing.
- Loops (
for x in y:), control flow (if z:) - Functions
- Factorial!
- CodingBat Python practice
- Differential equations, Simple Euler method to solve
- Numerical Convergence. How and why and when.
- Runge-Kutta RK4 and convergence
- Runge-Kutta RK4
- Numpy arrays
- Projects brainstorm
- Fleshing out a project idea (how to cook an egg)
- Flesh out a project
scipy.integrate.solve_ivp
- Improve a project outline
- Kinetic Monte Carlo
- Debugging
- Kinetic Monte Carlo
- Regression
- How we did the regression homework
- Some talk of projects
- Sensitivity analysis
- Git and github
- Git and github
- PDEs
- Linux
- Projects
Bash and sign up for Discovery
- Discovery
- Population Balances
Day before Thanksgiving recess
- Bash
- Book reviews
- Rabbits and foxes diffusing
- PDEs
- Bash
- LaTeX
- Population Balance Modeling
- Cantera
- Pandas (polyethylene?)
- machine learning
- CodingBat
- VSCode